Method for controlling direct current bus voltage in hydrogen production system based on voltage-power dynamic mapping

Abstract

The invention belongs to the technical field of electrolytic hydrogen production, and provides a voltage-power dynamic mapping-based control method for direct-current bus voltage in a hydrogen production system. The method comprises the following steps of: selecting charge state deviation of an energy storage unit and a power demand of a PEM electrolytic bath as input variables of a fuzzy controller, and taking correction of a droop coefficient as an output variable; dividing the domain of discourse of each variable into five fuzzy subsets; dividing each variable into five fuzzy interval grades, and constructing a multivariable partition dynamic mapping relation, so that each input combination is uniquely mapped to a specific output decision; constructing a voltage-power dynamic mapping relation, and stipulating the correction of the droop coefficient under different input combinations; and the charge state deviation of the energy storage unit and the power requirement of the PEM electrolytic cell are collected in real time, the droop coefficient is updated based on the correction amount of the droop coefficient, and the updated droop coefficient is converted into a direct current bus to provide an optimized voltage reference instruction for the energy storage unit.

Description

Technical Field

[0001] This invention relates to the field of electrolytic hydrogen production technology, and in particular to a method for controlling the DC bus voltage in a hydrogen production system based on voltage-power dynamic mapping. Background Technology

[0002] The DC bus voltage stability of off-grid photovoltaic hydrogen production systems directly determines system safety and hydrogen production efficiency. Due to the complex coupling characteristics between renewable energy output fluctuations and power converters, traditional droop control suffers from slow transient response and large steady-state deviation, and existing adaptive strategies still have insufficient control performance under complex operating conditions.

[0003] Currently, traditional droop control is commonly used. This is a distributed control strategy based on local measurements, achieving autonomous power allocation by simulating the "power-frequency-static characteristics" of a synchronous generator without inter-unit communication. However, its dynamic response performance is insufficient, and its scenario adaptability is poor. Other researchers employ adaptive droop control based on SOC parameters, dynamically adjusting the droop coefficient through the energy storage unit's state of charge (SOC) (e.g., designing the droop coefficient as a function of SOC) to achieve preliminary optimization of power allocation and voltage deviation. However, it relies on fixed-parameter logic and suffers from insufficient dynamic response. Still others use multi-source coordinated droop control based on voltage partitioning, dividing the DC bus voltage into multiple intervals. Different intervals trigger different droop control modes for different energy storage units (batteries, fuel cells, electrolyzers) to achieve source-storage-load coordination. However, its mode switching exhibits hysteresis, and the nonlinear coupling problem remains unresolved.

[0004] Therefore, the above-mentioned droop control scheme has the following drawbacks: 1. Insufficient dynamic adaptability: Traditional droop control relies on a fixed droop coefficient. When renewable energy changes dynamically (such as sudden changes in light intensity or wind speed), the bus voltage transient response is slow and the steady-state deviation is large, making it difficult to effectively track rapid power fluctuations.

[0005] 2. Single input variable, neglecting load dynamics: Existing adaptive control strategies (such as SOC-based adaptive droop control) mainly rely on a single variable (such as SOC) for parameter adjustment. Although some studies have considered load power, they have not specifically addressed the power demand of the PEM electrolyzer in the hydrogen production system to dynamically adjust the droop coefficient. This single-input control scheme is difficult to achieve accurate power balance and voltage support under complex operating conditions where both the source and load sides fluctuate drastically.

[0006] 3. Limited steady-state accuracy: Although adaptive droop control based on SOC correlation parameters can reduce steady-state voltage deviation, it relies on fixed parameter adjustment logic and still has insufficient control accuracy when dealing with strong nonlinear fluctuations.

[0007] 4. Weak anti-interference robustness: The existing adaptive droop control strategy still needs to improve its control performance in environments with multivariable coupling (such as the nonlinear characteristics of power converters) and strong interference (such as the drastic fluctuations of renewable energy).

[0008] 5. Lack of scenario adaptability: Existing methods have not been optimized for the unique operating characteristics of off-grid photovoltaic hydrogen production systems (such as electrolyzer load characteristics and high volatility), which limits the effectiveness of control strategies in practical applications. Summary of the Invention

[0009] The purpose of this invention is to provide a control method for DC bus voltage in a hydrogen production system based on voltage-power dynamic mapping, which can improve voltage stability under complex operating conditions, optimize transient and steady-state performance, achieve efficient adaptive allocation of energy storage power, and enhance the control robustness of nonlinear systems.

[0010] The technical solution adopted by this invention to solve its technical problem is as follows: A control method for DC bus voltage in a hydrogen production system based on voltage-power dynamic mapping is used to optimize the droop control strategy adopted by the bidirectional DC-DC converter of the energy storage unit in the hydrogen production system. The method includes the following steps: The state of charge deviation of the energy storage unit and the power demand of the PEM electrolyzer are selected as the input variables of the fuzzy controller, and the correction amount of the droop coefficient is used as the output variable of the fuzzy controller. The universes of discourse for both the input and output variables are divided into five fuzzy subsets respectively; The input and output variables are divided into five fuzzy interval levels respectively, and a multivariate partition dynamic mapping relationship is constructed so that each input combination is uniquely mapped to a specific output decision. Construct a dynamic voltage-power mapping relationship and specify the correction amount of the droop coefficient under different input combinations; The system collects the state-of-charge deviation of the energy storage unit and the power demand of the PEM electrolyzer in real time, updates the droop coefficient based on the correction amount of the droop coefficient under different input combinations, and converts the updated droop coefficient into an optimized voltage reference command for the DC bus to provide to the energy storage unit.

[0011] In some embodiments, the selected state of charge deviation is denoted as ΔSOC, the power requirement of the PEM electrolyzer is denoted as P_PEM, and the correction amount for the droop coefficient is denoted as Δk, where: ΔSOC=SOC i -SOC avg Among them, SOC i For the i-th energy storage unit, the real-time state of charge (SOC) is... avg This represents the average real-time state of charge of all energy storage units in the hydrogen production system. P_PEM represents the real-time power requirement of the PEM electrolyzer, expressed in per-unit form, based on the rated power. Δk is the droop coefficient k of the i-th energy storage unit. i The real-time correction amount.

[0012] In some embodiments, dividing the universes of discourse of the input and output variables into five fuzzy subsets respectively means: The universe of discourse for the input variables of the energy storage unit's state-of-charge deviation ΔSOC is divided into five fuzzy subsets: {NB (negative large), NS (negative small), Z0 (zero), PS (positive small), PB (positive large)}; The universe of discourse of the input variables for the power demand P_PEM of the PEM electrolyzer is divided into five fuzzy subsets: {NB (negative large), NS (negative small), Z0 (zero), PS (positive small), PB (positive large)}; The universe of discourse of the output variable, which is the correction amount Δk for the droop coefficient, is divided into five fuzzy subsets: {LB (significantly decreases), LS (slightly decreases), NC (unchanged), HS (slightly increases), HB (significantly increases)}.

[0013] In some embodiments, dividing the input and output variables into five fuzzy interval levels and constructing a multivariate partitioned dynamic mapping relationship so that each input combination uniquely maps to a specific output decision means: When the input range of ΔSOC is [-1, 1], that is: NB (Negative Large): [-1.0, -0.6, -0.2], centered at -0.6; NS (Negative Small): [-0.6, -0.3, 0.0], centered at -0.3; ZO (zero): [-0.2, 0.0, 0.2], centered at 0.0; PS (smallest): [0.0, 0.3, 0.6], centered at 0.3; PB (Zhengda): [0.2, 0.6, 1.0], centered at 0.6. When the input range of P_PEM is [4.0, 4.2], that is: NB (Negative High): [4.00, 4.04, 4.06], with the center at 4.04, indicating extremely low power demand; NS (Negative Small): [4.04, 4.08, 4.12], centered at 4.08; ZO (zero): [4.10, 4.12, 4.14], with the center at 4.12, indicating the vicinity of the rated power requirement; PS (smallest): [4.12, 4.14, 4.16], centered at 4.14; PB (Zhengda): [4.14, 4.18, 4.20], with the center at 4.18, indicating extremely high power demand; Then, the output range of Δk remains unchanged. At this time: LB (significantly reduced): [-1.0, -0.6]; LS (slightly decreased): [-0.8, -0.2]; NC (unchanged): [-0.4, 0.4]; HS (slightly increased): [0.2, 0.8]; HB (significantly increased): [0.6, 1.0].

[0014] In some embodiments, the construction of the voltage-power dynamic mapping relationship and the specification of the correction amount of the droop coefficient under different input combinations refer to: When ΔSOC is NB / NS and P_PEM is PS / PB, it indicates that the system is short of energy and has high load demand. At this time, the droop factor is increased significantly, that is, Δk is HB. When ΔSOC is PS / PB and P_PEM is NB / NS, it indicates that the system has excess energy and low load demand. At this time, the droop factor is significantly reduced, i.e., Δk is LB. When ΔSOC is ZO and P_PEM is ZO, it indicates that the system state is close to equilibrium. At this time, the droop coefficient remains unchanged, that is, Δk is NC.

[0015] In some embodiments, after real-time acquisition of the state-of-charge deviation of the energy storage unit and the power demand of the PEM electrolyzer, the real-time correction amount Δk of the droop coefficient is obtained.

[0016] In some embodiments, after obtaining the real-time correction amount Δk of the droop coefficient, the droop coefficient is updated based on the correction amount of the droop coefficient under different input combinations. The updating of the droop coefficient based on the correction amount of the droop coefficient under different input combinations refers to: Obtain the initial droop coefficient k i0 Then, add it to the real-time correction amount Δk of the obtained droop coefficient to obtain the updated droop coefficient k. i =k i0 +Δk.

[0017] In some embodiments, the updated droop coefficient k i It is applied in real time to the droop control equation of the corresponding energy storage unit to dynamically adjust its voltage-power characteristics.

[0018] The beneficial effects of this invention are: This invention can construct a multivariable partitioned dynamic mapping relationship with the state-of-charge deviation of the energy storage unit and the power demand of the PEM electrolyzer as inputs and the correction amount of the droop coefficient as output, replacing the traditional fixed parameter logic. By dynamically correcting the droop coefficient, the coupling between the nonlinearity of the power converter and the bus voltage fluctuation is weakened in real time. At the same time, this invention can also dynamically allocate the energy storage weight based on the voltage-power dynamic mapping relationship to achieve the balance of power allocation. Attached Figure Description

[0019] Figure 1 This is a flowchart of the DC bus voltage control method in a hydrogen production system based on voltage-power dynamic mapping in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the composition and structure of the hydrogen production system in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram comparing voltage stability when illumination drops to 0 in Embodiment 1 of the present invention; Figure 4 This is a schematic diagram comparing voltage stability under sudden increases in illumination in Embodiment 1 of the present invention; Figure 5 This is a schematic diagram of the power changes of each module in the system where the illumination suddenly drops to 0 in Embodiment 1 of the present invention; Figure 6 This is a schematic diagram showing the power changes of each module in the system during a sudden increase in illumination in Embodiment 1 of the present invention; Figure 7 This is a schematic diagram of the DC bus voltage change under a large disturbance in Embodiment 1 of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0021] Example 1

[0022] This embodiment provides a control method for the DC bus voltage in a hydrogen production system based on voltage-power dynamic mapping. It optimizes the droop control strategy employed by the bidirectional DC-DC converter in the energy storage unit of the hydrogen production system. The flowchart is shown below. Figure 1 The method may include the following steps: S1. Select the state of charge deviation of the energy storage unit and the power demand of the PEM electrolyzer as the input variables of the fuzzy controller, and use the correction amount of the droop coefficient as the output variable of the fuzzy controller. S2. Divide the universes of discourse of the input and output variables into five fuzzy subsets respectively; S3. Divide the input and output variables into five fuzzy interval levels respectively, construct a multivariate partition dynamic mapping relationship, so that each input combination is uniquely mapped to a specific output decision; S4. Construct a dynamic voltage-power mapping relationship and specify the correction amount of the droop coefficient under different input combinations; S5. Real-time acquisition of the state of charge deviation of the energy storage unit and the power demand of the PEM electrolyzer, and updating the droop coefficient based on the correction amount of the droop coefficient under different input combinations, and converting the updated droop coefficient into an optimized voltage reference command for the DC bus to provide to the energy storage unit.

[0023] See Figure 2 In this context, MPPT control represents power point tracking control, PV represents photovoltaic panels, and L represents... bo L represents a boost inductor. i and L Bu S1, S2, S3, and S4 represent filter inductors, S1, S2, S3, and S4 represent switching transistors, VD1 and VD2 represent diodes, and R represents... bus C represents the parasitic resistance of the DC bus capacitor. Bo C i C bus C Bu This represents the supporting capacitor. This embodiment optimizes the droop control strategy used by the bidirectional DC-DC converter of the energy storage unit in the system. By introducing a dynamic mapping mechanism based on fuzzy algorithms, the droop coefficient is adaptively adjusted in real time according to the SOC deviation and the power demand of the electrolyzer. All other control strategies in the system, including the MPPT control of the photovoltaic array, the constant power control of the electrolyzer, and the underlying current / voltage inner loop control of each power converter, remain unchanged from their original design. Their core parameters and control logic are not adjusted. This embodiment only provides optimized voltage reference commands to the energy storage converter, thereby achieving seamless collaboration with all existing and mature control modules in the system. Without significantly altering the existing system architecture, it significantly improves the stability of the DC bus voltage and the rationality of power distribution.

[0024] In this embodiment, in order to achieve accurate and adaptive adjustment of the droop coefficient, it is necessary to capture key dynamic variables that reflect the system's energy balance state and load demand.

[0025] Therefore, the state of charge deviation ΔSOC of the energy storage system (reflecting the balance of energy reserves within the system) and the power demand P_PEM of the PEM electrolyzer (reflecting the intensity of external load demand) are selected as the two input variables of the fuzzy controller, and the correction amount Δk of the droop coefficient is used as the output variable.

[0026] Therefore, in this embodiment, the selected state of charge deviation is denoted as ΔSOC, the power requirement of the PEM electrolyzer is denoted as P_PEM, and the correction amount of the droop coefficient is denoted as Δk, where: ΔSOC=SOC i -SOC avg Among them, SOC i For the i-th energy storage unit, the real-time state of charge (SOC) is... avg This represents the average real-time state of charge of all energy storage units in the hydrogen production system. P_PEM represents the real-time power requirement of the PEM electrolyzer, which can be expressed in per-unit form, based on the rated power. Δk is the droop coefficient k of the i-th energy storage unit. i The real-time correction amount.

[0027] It should be noted that in order to convert precise input values ​​into semantic information that can be processed by fuzzy logic and to balance control precision and system complexity, all variables need to be fuzzified. A five-level fuzzy set partitioning is adopted, which is an optimal balance between control fineness and rule base complexity (5x5=25 rules). Too few levels will lead to coarse control, while too many levels will cause the rule base to expand, which is not conducive to engineering implementation.

[0028] Therefore, in this embodiment, dividing the universes of discourse of the input and output variables into five fuzzy subsets respectively means: The universe of discourse for the input variables of the energy storage unit's state-of-charge deviation ΔSOC is divided into five fuzzy subsets: {NB (negative large), NS (negative small), Z0 (zero), PS (positive small), PB (positive large)}; The universe of discourse of the input variables for the power demand P_PEM of the PEM electrolyzer is divided into five fuzzy subsets: {NB (negative large), NS (negative small), Z0 (zero), PS (positive small), PB (positive large)}; The universe of discourse of the output variable, which is the correction amount Δk for the droop coefficient, is divided into five fuzzy subsets: {LB (significantly decreases), LS (slightly decreases), NC (unchanged), HS (slightly increases), HB (significantly increases)}.

[0029] To achieve precise reasoning based on fuzzy logic, precise input quantities must be transformed into fuzzy semantic concepts. In this embodiment, both input variables (ΔSOC and P_PEM) are independently divided into fuzzy intervals, so that each input combination can be uniquely mapped to a specific output decision (Δk). This "dual-input-single-output" mapping structure is the basis for achieving multivariable adaptive control in this embodiment.

[0030] By using the interval partitioning method, the input variables can be divided into five interval levels {NB, NS, ZO, PS, PB}, and the output variables can be divided into {LB, LS, NC, HS, HB}, corresponding to different numerical ranges.

[0031] Therefore, in this embodiment, the step of dividing the input and output variables into five fuzzy interval levels and constructing a multivariate partitioned dynamic mapping relationship, so that each input combination is uniquely mapped to a specific output decision, is explained in detail using the following exemplary quantization range: When the input range of ΔSOC is [-1, 1], that is: NB (Negative Large): [-1.0, -0.6, -0.2], centered at -0.6; NS (Negative Small): [-0.6, -0.3, 0.0], centered at -0.3; ZO (zero): [-0.2, 0.0, 0.2], centered at 0.0; PS (smallest): [0.0, 0.3, 0.6], centered at 0.3; PB (Zhengda): [0.2, 0.6, 1.0], centered at 0.6. When the input range of P_PEM is [4.0, 4.2], that is: NB (Negative High): [4.00, 4.04, 4.06], with the center at 4.04, indicating extremely low power demand; NS (Negative Small): [4.04, 4.08, 4.12], centered at 4.08; ZO (zero): [4.10, 4.12, 4.14], with the center at 4.12, indicating the vicinity of the rated power requirement; PS (smallest): [4.12, 4.14, 4.16], centered at 4.14; PB (Zhengda): [4.14, 4.18, 4.20], with the center at 4.18, indicating extremely high power demand; Then, the output range of Δk remains unchanged. At this time: LB (significantly reduced): [-1.0, -0.6]; LS (slightly decreased): [-0.8, -0.2]; NC (unchanged): [-0.4, 0.4]; HS (slightly increased): [0.2, 0.8]; HB (significantly increased): [0.6, 1.0].

[0032] Through the above division, the system state (determined by the precise values ​​of ΔSOC and P_PEM) can be mapped to a regular grid consisting of 25 possible combinations, each of which uniquely corresponds to a specific control output Δk.

[0033] In practical applications, fuzzy rules are semantic summaries of system control strategies based on expert experience. Their core is to determine the adjustment direction and magnitude of the output variable according to different combinations of the states of two input variables, so as to achieve the goals of voltage stability and power balance.

[0034] Therefore, in this embodiment, the construction of the voltage-power dynamic mapping relationship and the specification of the correction amount of the droop coefficient under different input combinations refer to: When ΔSOC is NB / NS and P_PEM is PS / PB, it indicates that the system is short of energy and has high load demand. At this time, the droop factor is increased significantly, i.e. Δk is HB, to force the energy storage unit to reduce discharge, protect the energy storage and prioritize voltage stability. When ΔSOC is PS / PB and P_PEM is NB / NS, it indicates that the system has excess energy and low load demand. At this time, the droop factor is significantly reduced, i.e., Δk is LB, to encourage the energy storage unit to absorb more surplus power. When ΔSOC is ZO and P_PEM is ZO, it indicates that the system state is close to equilibrium. At this time, the droop coefficient remains unchanged, that is, Δk is NC.

[0035] See Table 1 for the detailed mapping rules: Table 1 Mapping Rules Table

[0036] For example, when the system detects ΔSOC = -0.4 (this value falls within the NS interval [-0.6, -0.2]) and P_PEM = 4.06 (this value falls within the NS interval [4.04, 4.09]), by referring to the table above, the control system will output Δk = HS (this label corresponds to the numerical range [0, 0.5]). This means that the system will calculate an accurate value (e.g., 0.3) within this output interval based on the preset membership function, thereby slightly increasing the droop coefficient.

[0037] It should be noted that in order to apply the established fuzzy rules to the real-time system and convert the fuzzy output into precise control commands, it is necessary to fuzzify the precise values ​​ΔSOC and P_PEM acquired in real time through the membership function, then perform inference based on the fuzzy rule base, and finally use defuzzification methods such as the centroid method to obtain a precise droop coefficient correction amount Δk.

[0038] Therefore, in this embodiment, after real-time acquisition of the state of charge deviation of the energy storage unit and the power demand of the PEM electrolyzer, the real-time correction amount Δk of the droop coefficient is obtained. After obtaining the real-time correction amount Δk of the droop coefficient, the droop coefficient is updated based on the correction amount of the droop coefficient under different input combinations.

[0039] In this embodiment, updating the droop coefficient based on the correction amount of the droop coefficient under different input combinations means: Obtain the initial droop coefficient k i0 Then, add it to the real-time correction amount Δk of the obtained droop coefficient to obtain the updated droop coefficient k. i =k i0 +Δk.

[0040] It should be noted that the updated droop coefficient k i It is applied in real time to the droop control equation of the corresponding energy storage unit to dynamically adjust its voltage-power characteristics.

[0041] Example 2 Based on Embodiment 1, this embodiment compares the control method for DC bus voltage in a hydrogen production system based on voltage-power dynamic mapping with existing droop control schemes, thereby achieving the following technical effects of the method in this embodiment: 1. Improve voltage stability under complex operating conditions: Solve the problem of voltage instability in traditional control systems under disturbances such as sudden changes in light intensity. See also Figure 3 and Figure 4 The simulation results show that under extreme conditions of sudden increase and decrease in illumination to 0, the method proposed in this embodiment can significantly reduce the voltage overshoot to 0.486% and 0.432%, respectively, which is much lower than the 0.996% and 0.825% of the traditional droop control. The stability is improved by more than 50%, proving that the method proposed in this embodiment can maintain voltage stability under various complex conditions.

[0042] 2. Optimize transient and steady-state performance: This addresses the problems of slow transient response and insufficient steady-state accuracy in existing methods. Referring to Tables 2, 3, and 4, the method proposed in this embodiment reduces transient voltage recovery time by 61% compared to traditional droop control schemes, improves it by 35% compared to the SOC-adaptive improvement scheme, and maintains a high-precision steady-state voltage deviation of ≤1% (traditional control >5%), achieving a simultaneous leap in response speed and stability accuracy.

[0043] Table 2 Comparison of three sagging control performances under sudden increases in light intensity.

[0044] Table 3 Comparison of sagging control performance under three conditions: sudden increase and decrease in light intensity.

[0045] Table 4 Performance Improvements

[0046] 3. Achieve efficient adaptive allocation of energy storage power: Solve the problem of uneven power distribution caused by traditional fixed droop coefficients. See also Figure 5 and Figure 6 The method proposed in this embodiment enables the power distribution of energy storage units to no longer rely on a simple capacity ratio as in traditional droop control schemes, but achieves dynamic adaptive distribution with a power distribution balance of >95%, effectively avoiding the overload risk of small-capacity energy storage units.

[0047] 4. Enhance the robustness of nonlinear system control: By introducing a multivariable dynamic mapping mechanism with SOC deviation and PEM electrolyzer power demand as inputs and the correction amount of the droop coefficient as output, the shortcomings of existing control in scenarios with strong disturbances and multivariable coupling are effectively addressed. See also Figure 7 Under multiple disturbances such as continuous fluctuations in illumination, the method proposed in this embodiment can effectively suppress voltage oscillations and exhibits control robustness far superior to traditional solutions.

[0048] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for controlling the DC bus voltage in a hydrogen production system based on voltage-power dynamic mapping, for optimizing the droop control strategy adopted by the bidirectional DC converter of the energy storage unit in the hydrogen production system, characterized in that, The method comprises the following steps: The state of charge deviation of the energy storage unit and the power demand of the PEM electrolyzer are selected as input variables of the fuzzy controller, and the correction amount of the droop coefficient is selected as an output variable of the fuzzy controller; The domains of the input variables and the output variable are respectively divided into five fuzzy subsets; The input variables and the output variables are respectively divided into five fuzzy interval levels, and a multi-variable partition dynamic mapping relationship is constructed, so that each input combination is uniquely mapped to a specific output decision; A voltage-power dynamic mapping relationship is constructed, and the correction amount of the droop coefficient under different input combinations is specified; The state of charge deviation of the energy storage unit and the power demand of the PEM electrolyzer are collected in real time, the droop coefficient is updated based on the correction amount of the droop coefficient under different input combinations, and the updated droop coefficient is converted into an optimized voltage reference instruction provided by the DC bus to the energy storage unit.

2. The method for control of DC bus voltage in a hydrogen production system based on voltage-power dynamic mapping as claimed in claim 1, wherein, The selected state of charge deviation is denoted as ΔSOC, the power demand of the PEM electrolyzer is denoted as P_PEM, and the correction amount of the droop coefficient is denoted as Δk, wherein: ΔSOC = SOC i -SOC avg wherein SOC i is the real-time state of charge of the i-th energy storage unit, and SOC avg is the average of the real-time state of charge of all energy storage units in the hydrogen production system; P_PEM is the real-time power demand of the PEM electrolyzer, which is in the form of a per-unit value and is based on the rated power; Δk is the droop coefficient k of the i-th energy storage unit i real-time correction amount of the i-th energy storage unit.

3. The method for controlling the DC bus voltage in a hydrogen production system based on voltage-power dynamic mapping according to claim 2, characterized in that, The domains of the input variables and the output variable are respectively divided into five fuzzy subsets, which means that: The domain of the input variable of the state of charge deviation ΔSOC of the energy storage unit is divided into five fuzzy subsets: {NB (negative large), NS (negative small), Z0 (zero), PS (positive small), PB (positive large)}; The domain of the input variable of the power demand P_PEM of the PEM electrolyzer is divided into five fuzzy subsets: {NB (negative large), NS (negative small), Z0 (zero), PS (positive small), PB (positive large)}; The domain of the output variable of the correction amount Δk of the droop coefficient is divided into five fuzzy subsets: {LB (large reduction), LS (small reduction), NC (unchanged), HS (small increase), HB (large increase)}.

4. The method for controlling the DC bus voltage in a hydrogen production system based on voltage-power dynamic mapping according to claim 3, characterized in that, The input variables and the output variables are respectively divided into five fuzzy interval levels, and a multi-variable partition dynamic mapping relationship is constructed, so that each input combination is uniquely mapped to a specific output decision, which means that: When the input range of ΔSOC is [-1, 1], that is: NB (negative large): [-1.0, -0.6, -0.2], centering at -0.6; NS (negative small): [-0.6, -0.3, 0.0], centering at -0.3; ZO (zero): [-0.2, 0.0, 0.2], centering at 0.0; PS (positive small): [0.0, 0.3, 0.6], centering at 0.3; PB (positive large): [0.2, 0.6, 1.0], centering at 0.6 When the input range of P_PEM is [4.0, 4.2], that is: NB (negative large): [4.00, 4.04, 4.06], centering at 4.04, indicating very low power demand; NS (negative small): [4.04, 4.08, 4.12], centering at 4.08; ZO (zero): [4.10, 4.12, 4.14], centering at 4.12, indicating near the rated power demand; PS (positive small): [4.12, 4.14, 4.16], centering at 4.14; PB (positive big): [4.14, 4.18, 4.20], centering at 4.18, indicating that the power demand is extremely high; Then, the output range of Δk remains unchanged, at this time: LB (large reduction): [-1.0, -0.6]; LS (small reduction): [-0.8, -0.2]; NC (unchanged): [-0.4, 0.4]; HS (small increase): [0.2, 0.8]; HB (large increase): [0.6, 1.0].

5. The method for control of DC bus voltage in a hydrogen production system based on voltage-power dynamic mapping as claimed in claim 3, wherein, The construction of the voltage-power dynamic mapping relationship and the regulation of the correction amount of the droop coefficient under different input combinations are as follows: When ΔSOC is NB / NS and P_PEM is PS / PB, it indicates that the system is short of energy and the load demand is high, at this time, the droop coefficient is greatly increased, that is, Δk is HB; When ΔSOC is PS / PB and P_PEM is NB / NS, it indicates that the system is in excess of energy and the load demand is low, at this time, the droop coefficient is greatly reduced, that is, Δk is LB; When ΔSOC is ZO and P_PEM is ZO, it indicates that the system state is close to balance, at this time, the droop coefficient remains unchanged, that is, Δk is NC.

6. The method for controlling the DC bus voltage in a hydrogen production system based on voltage-power dynamic mapping according to any of claims 1-5, characterized in that, After the state of charge deviation of the energy storage unit and the power demand of the PEM electrolytic cell are collected in real time, the real-time correction amount Δk of the droop coefficient is obtained.

7. The method for controlling the DC bus voltage in a hydrogen production system based on voltage-power dynamic mapping according to claim 6, characterized in that, After the real-time correction amount Δk of the droop coefficient is obtained, the droop coefficient is updated based on the correction amount of the droop coefficient under different input combinations; The updating of the droop coefficient based on the correction amount of the droop coefficient under different input combinations is as follows: Obtain initial droop coefficient k i0 and add it to the real-time correction amount Δk of the obtained droop coefficient to obtain the updated droop coefficient k i =k i0 +Δk.

8. The method for controlling the DC bus voltage in a hydrogen production system based on voltage-power dynamic mapping according to claim 7, characterized in that, Updated droop coefficient k i The updated droop coefficient k is applied in real time to the droop control equation of the corresponding energy storage unit for dynamically adjusting its voltage-power characteristic.

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